Conversational Recommendation

The goal of recommendation systems is to help users find items of
interest, and thus they must eliminate some choices from consideration
while keeping others. To achieve this, they must
either know or determine the interests of the current user, then
present items that satisfy those interests.
One of the most widely used approaches for information acquisition in
recommendation systems is the use of a simple form (usually a single
query box) that the user fills in. Coupled with this, a common
presentation format is the ranked list approach. In this
scheme, the system presents a usually long list of results, ordered by
their predicted relevance to the user. The combined technique of
information acquisition through a form and presentation in a list
originated in information retrieval, where it was used to help find
documents or reports during library research. The basic method, which
we refer to as the ranked list approach, is more
general, however, and it underlies the simpler search engines
for the World Wide Web, which are used regularly for many different
selection tasks.
In contrast, the ADAPTIVE PLACE ADVISOR supports
conversations as its mode of interaction. In this approach,
one conversational participant, which we call the inquirer, asks
a second participant, which we call the advisor, for assistance
in selecting an item. For example, if the inquirer is deciding on a
restaurant at which to have dinner, the conversation may proceed as
follows:
1. Inquirer: Where do you think I should eat tonight?
2. Advisor: What type of food would you like?
3. Inquirer: What types are there?
4. Advisor:
You can say things like Chinese, Indian, and Mediterranean.
5. Inquirer: Oh, maybe a cheap Indian place.
6. Advisor: What kind of parking would you like?
7. Inquirer: I don't care, as long as it's in Palo Alto.
8. Advisor: I'm sorry, I don't know of any restaurants like that,
would you like to search for any price?
9. Inquirer: No, I think I'd like Chinese instead.
10. Advisor: How does Mandarin Gourmet on 420 Ramona
sound?1
11. Inquirer: No, what else do you have?
12. Advisor: How about Jing-Jing Szechwan Hunan Gourmet on 443 Emerson Street?
13. Inquirer: Sure, that sounds fine.
We will refer to systems that mimic this approach to recommendation as
conversational recommendation systems.
The interaction supported by conversational recommendation systems is
quite different from that found in the ranked list approach. The most
important distinction is that the interaction takes the form of a
sequence of questions, most designed to eliminate some items from
consideration. Answering these questions plays a similar role to
giving keywords in a form, but the aim is to remove alternatives
rather than to rank results in a list. This conversational process
can also help the inquirer better understand his own desires, since
thinking about possible questions and answers may clarify goals in
ways that keyword entry does not encourage. In addition, a
conversation can support user variability by letting the user control
item and attribute choices in each conversation. Finally, the
inquirer never hears about a complete item until at most a few choices
remain. This avoids presenting a long list of items and lets the user
narrow down the choices in an iterative, manageable fashion.
Such dialogues seem better for recommendations that must be delivered
by speech rather than visually, for example those
engaged in while the inquirer is driving.
They also seem ideal, independent of modality,
for tasks like destination selection or help-desk support
[36,4],
in which the user needs to converge on at most a few items. On the
other hand, keyword entry and ranked list methods seem more
appropriate in situations where the user prefers to provide all
requirements at once, in situations
where information can be presented visually, and
in situations where the user may want to examine many options.
By eliminating options, conversational recommendation systems
ultimately direct their users to a suitable solution. However, such a
conversation can become tiring and the quality of the first result
returned may not be acceptable for each user. Just as interactions
with a friend who knows your concerns can be more directed and produce
better results than those with a stranger, dialogues with
a conversational advisor should
become more efficient and effective over time. Our goals
for user modeling include improvement of the subjective quality and
effectiveness of both the results (found items) and the conversation
that leads to these results. For example, after several conversations
with the inquirer above, a new interaction may proceed as follows,
where the question about parking has been eliminated and the item
presentation order has changed:
1. Inquirer: Where do you think I should eat tonight?
2. Advisor: What type of food would you like?
3. Inquirer: Oh, maybe a Chinese place.2
4. Advisor: What city do you prefer?
5. Inquirer: Do you have something in Palo Alto?
6. Advisor: How does Jing-Jing Szechuan Gourmet on 443 Emerson
sound?
7. Inquirer: Sure, that sounds fine.
We turn next to our design choices concerning the management of such conversations.
Next:Conversation via Dialogue Management Up:Personalized Conversational Recommendation Systems Previous:PersonalizationCindi Thompson2004-03-29